Prediction of HIV Mortality in Thailand using Three Data Sets from the National AIDS Program Database

Abstract

HIV continues to hinder the growth and development of a country and identifying the mortality rate will help to address this problem. Therefore, we sought to examine HIV mortality in Thailand in the years 2014 and 2015. A retrospective study was conducted to investigate HIV mortality in all regions of Thailand. For the reliability of this study, the data were drawn from two main sources, Bureau of Registration and Administration (BORA) and the National Health Security Office (NHSO) of Thailand. A total of 23,243 deaths were recorded in years 2014 and 2015. Negative binomial regression was used to predict and compare HIV mortality rates by year, age group and gender. The overall HIV mortality accounted for 2.6% of all reported deaths, representing 18.3 deaths per 100,000 populations. Among HIV deaths, 91% of them participated in the National AIDS Program (NAP) and 56% of them received Antiretroviral Therapy (ART). There was statistically significant effect, with males having a higher death rates than females. Despite the relevance of reduction of HIV mortality in Thailand, our study clearly shows that HIV mortality in Thailand is much dependent on age and gender. Thus, we suggest continued effort is needed to address gender and age difference.

Effect of land cover change and elevation on decadal trend of land surface temperature: a linear model with sum contrast analysis

“…This method of weighted sum contrasts collaborating with linear and logistic regression had been used in many preceding studies. It used to compare blood lead levels among children in Pattani river, Thailand, 22 and investigated HIV mortality by age group and gender in Thailand between 2014 and 2015 (Tulu et al 2020).…”

Estimating Ground-Level Hourly PM2.5 Concentrations in Thailand Using Satellite Data: A Log-Linear Model With Sum Contrast Analysis

“…For instance, utilized weighted sum contrasts in linear regression to evaluate the influence of land-cover transformation and Elevation (EV) on decadal changes in Land Surface Temperature (LST) by comparing the adjusted mean of all factors [27]. Conducted a comparison of blood lead levels among children in the Pattani River region of Thailand [28], while the examined HIV mortality by age group and gender in Thailand between 2014 and 2015 [29]. Additionally, employed weighted sum contrasts logistic regression to investigate landuse change in Thailand [30], [31], [32].…”